Last updated: 2025-11-20

Checks: 6 1

Knit directory: phenotype_genotype/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(20240315) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version bca1be0. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .RData
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/
    Ignored:    src/

Untracked files:
    Untracked:  11_nested_pcr_PALM08_E.Rmd
    Untracked:  11_nested_pcr_PALM08_E.html
    Untracked:  12_nested_pcr_PALM07_E.Rmd
    Untracked:  12_nested_pcr_PALM07_E.html
    Untracked:  README-RSTUDIO.txt
    Untracked:  figure/

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.


Objective

Genotype data integration of ONT enrichment experiment with 10X expression data for AML4 sample

Genotype data

The genotype data has been analyzed using NanoGen, a custom pipeline designed to call mutations from targeted enrichment data. NanoGen follows a multi-step process to ensure accurate mutation detection. It starts by using Blaze to identify cell barcodes, ensuring that each cell’s data is correctly assigned. It then collapses Unique Molecular Identifiers (UMIs) to eliminate PCR duplicates and other biases, allowing for a more accurate representation of the original sample.

One key feature of NanoGen is its ability to call consensus at the base level, rather than at the sequence level, providing a more precise identification of mutations. NanoGen applies a negative binomial model to distinguish between true mutated reads and background noise, improving the reliability of mutation calls. To address the issue of mutated allele dropout, NanoGen imposes a strict threshold: it requires at least five distinct wild-type (WT) UMIs for a cell to be confidently classified as wild-type (WT).

Data exploration

First, we identify and retain only mutations that overlap with the WES data. For each candidate mutation, we calculate its cellular fraction. Next, we filter out any genes with fewer than 50 genotyped cells.

Information from the log files:

Below, we present the correlation between the cellular fraction, defined as VAF * 2, in WES and the cellular fraction in the target enrichment. As demonstrated, the target enrichment effectively recapitulates the cellular fraction observed in WES.

Mutation co-occurence

Next, we quantified the number of cells with multiple genotyped mutations, as shown in the tables below. We present the results for different mutation combinations. For this specific samples, we will look at these genes since they are the most genotyped: BAX ADCK1 DPF2 IPO9 RUNX1


R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] stringi_1.5.3          ReactomePA_1.34.0      clusterProfiler_3.18.1
 [4] org.Hs.eg.db_3.12.0    AnnotationDbi_1.52.0   IRanges_2.24.1        
 [7] S4Vectors_0.28.1       Biobase_2.50.0         BiocGenerics_0.36.1   
[10] plotly_4.9.3           maftools_2.6.05        mclust_5.4.7          
[13] SeuratObject_4.0.0     Seurat_4.0.0           forcats_0.5.1         
[16] stringr_1.4.0          dplyr_1.0.4            purrr_0.3.4           
[19] readr_1.4.0            tidyr_1.1.2            tibble_3.0.6          
[22] ggplot2_3.3.3          tidyverse_1.3.0       

loaded via a namespace (and not attached):
  [1] reticulate_1.18      tidyselect_1.1.0     RSQLite_2.2.3       
  [4] htmlwidgets_1.5.3    grid_4.0.3           BiocParallel_1.24.1 
  [7] Rtsne_0.15           scatterpie_0.1.5     munsell_0.5.0       
 [10] codetools_0.2-18     ica_1.0-2            DT_0.17             
 [13] future_1.21.0        miniUI_0.1.1.1       withr_2.4.1         
 [16] colorspace_2.0-0     GOSemSim_2.16.1      knitr_1.31          
 [19] rstudioapi_0.13      ROCR_1.0-11          tensor_1.5          
 [22] DOSE_3.16.0          listenv_0.8.0        labeling_0.4.2      
 [25] git2r_0.28.0         polyclip_1.10-0      bit64_4.0.5         
 [28] farver_2.0.3         rprojroot_2.0.2      downloader_0.4      
 [31] parallelly_1.23.0    vctrs_0.3.6          generics_0.1.0      
 [34] xfun_0.21            R6_2.5.0             graphlayouts_0.7.1  
 [37] spatstat.utils_2.0-0 cachem_1.0.3         fgsea_1.16.0        
 [40] assertthat_0.2.1     promises_1.1.1       scales_1.1.1        
 [43] ggraph_2.0.4         enrichplot_1.10.2    gtable_0.3.0        
 [46] globals_0.14.0       goftest_1.2-2        tidygraph_1.2.0     
 [49] workflowr_1.6.2      rlang_0.4.10         splines_4.0.3       
 [52] lazyeval_0.2.2       broom_0.7.4          checkmate_2.0.0     
 [55] BiocManager_1.30.10  yaml_2.2.1           reshape2_1.4.4      
 [58] abind_1.4-5          modelr_0.1.8         crosstalk_1.1.1     
 [61] backports_1.2.1      httpuv_1.5.5         qvalue_2.22.0       
 [64] tools_4.0.3          ellipsis_0.3.1       RColorBrewer_1.1-2  
 [67] ggridges_0.5.3       Rcpp_1.0.6           plyr_1.8.6          
 [70] rpart_4.1-15         deldir_0.2-9         pbapply_1.4-3       
 [73] viridis_0.5.1        cowplot_1.1.1        zoo_1.8-8           
 [76] haven_2.3.1          ggrepel_0.9.1        cluster_2.1.0       
 [79] fs_1.5.0             magrittr_2.0.1       data.table_1.13.6   
 [82] scattermore_0.7      DO.db_2.9            reactome.db_1.74.0  
 [85] lmtest_0.9-38        reprex_1.0.0         RANN_2.6.1          
 [88] fitdistrplus_1.1-3   matrixStats_0.58.0   hms_1.0.0           
 [91] patchwork_1.1.1      mime_0.9             evaluate_0.14       
 [94] xtable_1.8-4         readxl_1.3.1         gridExtra_2.3       
 [97] compiler_4.0.3       KernSmooth_2.23-18   crayon_1.4.1        
[100] shadowtext_0.0.7     htmltools_0.5.1.1    mgcv_1.8-33         
[103] later_1.1.0.1        lubridate_1.7.9.2    DBI_1.1.1           
[106] tweenr_1.0.1         dbplyr_2.1.0         rappdirs_0.3.3      
[109] MASS_7.3-53          Matrix_1.3-2         cli_2.3.0           
[112] igraph_1.2.6         pkgconfig_2.0.3      rvcheck_0.1.8       
[115] xml2_1.3.2           rvest_0.3.6          digest_0.6.27       
[118] sctransform_0.3.2    RcppAnnoy_0.0.18     graph_1.68.0        
[121] spatstat.data_2.0-0  rmarkdown_2.6        cellranger_1.1.0    
[124] leiden_0.3.7         fastmatch_1.1-0      uwot_0.1.10         
[127] shiny_1.6.0          graphite_1.36.0      lifecycle_0.2.0     
[130] nlme_3.1-152         jsonlite_1.7.2       viridisLite_0.3.0   
[133] pillar_1.4.7         lattice_0.20-41      fastmap_1.1.0       
[136] httr_1.4.2           survival_3.2-7       GO.db_3.12.1        
[139] glue_1.4.2           spatstat_1.64-1      png_0.1-7           
[142] bit_4.0.4            ggforce_0.3.2        blob_1.2.1          
[145] memoise_2.0.0        irlba_2.3.3          future.apply_1.7.0